Mark Laurence, Solution Architecture & Technology Lead Europe
Walking away from COMPUTEX 2026, I found myself reflecting on a question that would have sounded almost absurd just a few years ago: what if building better AI models is no longer the industry’s biggest challenge?
For much of the last decade, the AI conversation has been driven by a race for capability. Larger models, more parameters, bigger datasets and increasingly powerful infrastructure have dominated both headlines and investment. That race will continue, but COMPUTEX made it clear that the next phase of AI adoption will be defined less by what models can do and more by how effectively organisations can deploy them in the real world.
Intel CEO Lip-Bu Tan touched on this theme during his keynote, focusing on open ecosystems, practical deployment models and infrastructure capable of supporting AI beyond the data centre. His message was that the next wave of AI will depend not only on more powerful silicon, but on the ability to bring compute, software and partner innovation together in deployable systems.
Nowhere was that more obvious than in the Intel Robotics and Edge AI Pavilion at TWTC Hall 1. The pavilion brought together healthcare robotics, humanoid systems, industrial automation, machine vision, edge computing and AI software. Individually, the demonstrations were impressive. Collectively, they showed how AI is moving beyond screens and into systems that can perceive, respond to and act within the physical world.
What stood out was the architectural complexity required to make these robots useful. Physical AI is fundamentally different from many of the workloads that have defined the current AI boom. It requires low-latency compute, real-time sensing, control systems, software frameworks, power efficiency, operational reliability and the ability to continuously improve within dynamic environments. In short, it requires an ecosystem.
Physical AI is moving from concept to deployment
One of the most memorable examples came from Onyx Healthcare, which showcased an AI-powered rehabilitation exoskeleton designed to help patients regain mobility. Powered by Intel Core Ultra processors and OpenVINO, the system uses edge AI to analyse movement and support patient motion in real time.
Humanoid systems from VinRobotics provided perhaps the most visible demonstration of Physical AI in action. While humanoid robots attract attention, their real significance is that they represent a broader shift towards machines that can interpret and interact with the world around them.
Arguably, though, some of the most commercially significant examples were in industrial applications. AAEON demonstrated an AI-driven PCB inspection platform where inspection happens locally, operational data is fed back into edge infrastructure, models are refined and improvements are redeployed into production workflows. Fogsphere showed how vision AI agents can deliver contextual awareness and operational intelligence in industrial environments.
These examples may not have the same visual impact as humanoid robots, but they are likely to be where many organisations see the greatest near-term value. One misconception in the market is that Physical AI will be defined primarily by humanoid robotics. In reality, its largest commercial impact may come from less visible systems: inspection platforms, machine vision, predictive maintenance, autonomous industrial processes and AI-enabled operational environments. This is where Intel’s broader platform story became especially relevant.
Not every AI workload belongs in the cloud
Across the pavilion, a consistent edge AI architecture was evident: Intel Core Ultra processors bringing together CPU, GPU and NPU resources for local AI processing; OpenVINO providing the software layer for optimisation and deployment; and edge infrastructure supporting orchestration, retraining and model management closer to where data is created.
Just as importantly, this was not presented as a theoretical roadmap. It was reinforced by a partner ecosystem already active across healthcare, manufacturing, robotics, retail and industrial automation, showing how edge AI can move from concept to commercial deployment at scale.
Manufacturing systems require low latency and predictable performance. Healthcare environments demand reliability, privacy and local decision-making. Industrial facilities often operate where connectivity constraints make cloud-dependent architectures impractical. As AI becomes embedded into operational processes, continuously moving data between edge locations and centralised infrastructure introduces cost, complexity and latency that many real-world environments cannot tolerate.
The next AI opportunity is integration
One of the more interesting examples of this shift was Gigabyte’s dense 1U platform supporting forty Intel Core Ultra nodes. It may not have drawn the same attention as the robotics demonstrations, but it highlighted a critical infrastructure trend. As organisations move beyond pilots, they will need distributed compute platforms capable of supporting inference, orchestration, optimisation and operational resilience across many locations.
For partners, integrators and technology providers, this creates a different opportunity from the one many anticipated during the early stages of the AI boom. Competitive advantage is becoming less about access to a particular model and more about the ability to integrate infrastructure, software, networking, security and operational technology into complete, deployable solutions.
Looking back on COMPUTEX 2026, my biggest takeaway was not any single announcement or demonstration. It was the growing recognition that AI is entering a new stage of maturity. The industry has spent years making machines capable of understanding information. The next challenge is enabling them to interact meaningfully with the world around them.
The race to build better models will continue, but after COMPUTEX 2026, it is increasingly clear that the race to deploy them effectively has already begun.
